1 Description of Data

Salmon stock-recruitment data for populations in Alaska, BC, and Puget Sound were compiled from various sources by C. Cunningham (AFSC).
Salmon SR data from Washington, Oregon, and California were compiled by J. Gosselin and B. Burke (NWFSC)
These two datasets were combined in late November, 2018.
Update: Chinook salmon data have multiple runs with the same stock identifier. Scripts must contain code to append run to stock if present.

2 Structure of Data

Data is now stored in object called AK-WCoast-Salmon-SR.csv

dat <- read.csv("AK-WCoast-Salmon-SR.csv", header=TRUE)

What is the structure of this data frame?

kable(head(dat), align="crr") %>% #, digits=c(3,1,1)) %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

stock.id	species	run	stock	region	sub.region	broodYr	spawn	rec	large.region
NA	Sockeye	NA	Bear	AK Peninsula	AK Peninsula	1980	238038	545500	BS
NA	Sockeye	NA	Bear	AK Peninsula	AK Peninsula	1981	214728	318386	BS
NA	Sockeye	NA	Bear	AK Peninsula	AK Peninsula	1982	104503	280870	BS
NA	Sockeye	NA	Bear	AK Peninsula	AK Peninsula	1983	172143	319246	BS
NA	Sockeye	NA	Bear	AK Peninsula	AK Peninsula	1984	108151	503626	BS
NA	Sockeye	NA	Bear	AK Peninsula	AK Peninsula	1985	170739	939836	BS

Recorded brood years run from 1922 to 2016.

3 Preliminary Calculations

First we need to add recruits-per-spawner and log recruits-per-spawner

dat$rps <- dat$rec/dat$spawn
dat$ln.rps <- log(dat$rps)
kable(head(dat), align="crr") %>% #, digits=c(3,1,1)) %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

stock.id	species	run	stock	region	sub.region	broodYr	spawn	rec	large.region	rps	ln.rps
NA	Sockeye	NA	Bear	AK Peninsula	AK Peninsula	1980	238038	545500	BS	2.291651	0.8292725
NA	Sockeye	NA	Bear	AK Peninsula	AK Peninsula	1981	214728	318386	BS	1.482741	0.3938924
NA	Sockeye	NA	Bear	AK Peninsula	AK Peninsula	1982	104503	280870	BS	2.687674	0.9886761
NA	Sockeye	NA	Bear	AK Peninsula	AK Peninsula	1983	172143	319246	BS	1.854540	0.6176364
NA	Sockeye	NA	Bear	AK Peninsula	AK Peninsula	1984	108151	503626	BS	4.656693	1.5383055
NA	Sockeye	NA	Bear	AK Peninsula	AK Peninsula	1985	170739	939836	BS	5.504519	1.7055693

4 Data Quality Control

We also need to screen out non-sensical values reported in the data, specifically:

ln.rps is infinite, i.e. spawners were reported as zero
ln.rps is NA, i.e. either rec or spawn was blank
We also filter filter brood years:
broodYr > 1950 as this is the start of the NPGO index.
broodYr <= 2010 to ensure all complete return of age classes for all species.

dat.2 <- dat %>% filter(!is.infinite(ln.rps), !is.na(ln.rps), broodYr>=1950, broodYr<=2010)

So, the total number of observations is: 8549.

4.1 Deal with multiple runs of Chinook to same river.

I noticed that on the West Coast there were multiple Chinook salmon run identifiers for the same stock by broodYr combination. We need to identify these instances and update the stock name so it is treated separately in subsequent data exploration and STAN stock-recruitment models.

As it turns out there are 2583 observations (years) our of 8549 for which a run is identified.

5 Explore Data

5.1 Number of stocks by species and region

sum.stocks_species.region <- dat.2 %>% group_by(species, region) %>% summarize('n.stocks'=length(unique(stock)))
# kable(sum.stocks_species.region, align="crr") %>% #, digits=c(3,1,1)) %>%
  # kable_styling(bootstrap_options = c("striped", "hover"))

g <- ggplot(sum.stocks_species.region, aes(x=region, y=n.stocks, fill=species)) +
       theme_linedraw() +
       scale_fill_colorblind() +
       geom_bar(stat='identity', position='stack') +
       theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
       ylab('Number of Stocks')
plot(g)

5.2 Number of Years of Data by species and region

sum.years_species.region <- dat.2 %>% group_by(species, region) %>% summarize('n.years'=n())
# kable(sum.years_species.region, align="crr") %>% #, digits=c(3,1,1)) %>%
  # kable_styling(bootstrap_options = c("striped", "hover"))

g <- ggplot(sum.years_species.region, aes(x=region, y=n.years, fill=species)) +
       theme_linedraw() +
       scale_fill_colorblind() +
       geom_bar(stat='identity', position='stack') +
       theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
       ylab('Number of Years with Data')
plot(g)

5.3 Number of Regions is now: 25.

6 Plot Stock-Recruitment Data

6.1 Species Combined

6.2 Chinook

6.2.1 Data

6.2.2 Data + Smooth

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

6.3 Sockeye

6.3.1 Data

6.3.2 Data + Smooth

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

6.4 Pink

6.4.1 Data

6.4.2 Data + Smooth

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

6.5 Chum

6.5.1 Data

6.5.2 Data + Smooth

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

6.6 Coho

6.6.1 Data

6.6.2 Data + Smooth

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Explore-Coastwide-Data

Dr. Curry J. Cunningham

2/5/2019